The invention relates to segmenting images, i.e. the process of classifying pixels in an image as being associated with features of interest. For example, embodiments of the invention relate to automatically segmenting cell nuclei in color-stained immunohistochemical (IHC) microscopy images.
Image segmentation is a fundamental tool in image processing. It is the partitioning of an image into regions (segments), usually to help identify objects of interest. Thus the fundamental aim of segmentation is to identify groups of pixels in an image that are associated with one or more objects of interest. For example, in analysing a color-stained IHC image, a user may be interested in studying cell nuclei, e.g. to determine a ratio of stained to unstained nuclei. To do this the user must first identify the nuclei. This can be time consuming and, if done manually, is prone to user-subjectivity. In many circumstances it is therefore desirable to use an automated process for segmenting images whereby images are numerically processed to identify objects of interest according to pre-defined segmentation criteria. The segmented image may then be analysed, for example by visual inspection through a step of displaying the image on a monitor with an indication of the segmentation, and/or by numerical image processing that takes account of the segmentation.
In many cases automatic image segmentation is difficult to implement reliably. This is particularly true for biological application where objects can display a wide range of morphological characteristics in different tissue samples and under different staining/imaging conditions. There are various known segmentation schemes which may be used, and in general the different schemes have different efficacies for different situations.
Some known image segmentation algorithms do not consider the spatial relationships between pixels in an image during the segmentation. Instead, pixels are classified according to groupings they form when their individual properties are plotted in a “feature space” which does not take account of spatial relationships. The “feature space” is characterised by parameters considered appropriate for differentiating between pixels associated with the object(s) of interest and other pixels. These approaches include common techniques such as thresholding, color depth reduction, histogram splitting and feature-space clustering. However, a drawback of these schemes for some applications is that the relative positions of the image pixels are not considered. This means potentially important/useful information is not used, so the segmentation may not as good as it could be.
There are also known segmentation algorithms which are based on spatial information. These may be based on techniques such as “region growing”, “split and merge”, “watershed”, “edge detection and linking”, and so on. These schemes consider both the relative positions of pixels and the similarities/differences among them. This can produce good results, but can work less well when boundaries between objects cannot be deduced purely from the information in the image itself, for example when there are multiple touching objects of the same image intensity.
When applied to digital microscope images of tissue samples, the goal of segmentation is usually to separate out the parts of the image corresponding to features such as cells, and their constituent parts such as nuclei and membranes. The inventor has found that existing segmentation schemes are often unable to deal well with these sorts of image. There is therefore a need for improved schemes for classifying pixels in an image as being associated with a feature of interest.